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Incorporating Machine Learning To Avoid Contact Center Crimes

Incorporating Machine Learning To Avoid Contact Center Crimes

As companies have gotten better at detecting and preventing online fraud in recent years, would-be criminals have redirected their efforts to the corporate contact center. Financial services companies and retailers, for example, are reporting a rise in contact center fraud covering fraud and data security, in large part because “fraudsters tend to take the path of least resistance.”

Some enterprises will want to move their contact centers closer and tighten controls and limit what’s outsourced. But others are looking for third-parties who can provide the kind of security services they can’t.”

Next Generation Tools To Prevent Contact Center Crimes

Contact center agents, after all, are presumably focused on making the customer happy, which makes them especially gullible to fraud attempts. The key to fighting the escalating battle against such fraud may not be human intervention but emerging technologies. It’s a technology arms race and today’s contact center industry needs to have next generation tools.

One such tool is a voice biometrics engine, capable of identifying an individual “voiceprint” based on traits as vocal tract length, mouth size and shape of nasal passage–physical characteristics even the savviest criminal should find difficult to impersonate. Many banks are intrigued [by voice biometrics] as a possible solution to better authenticate their customers. Some focus on all customers while other advocate screening calls against a hot file of voice prints of callers who committed fraud previously.

AI Combined With Machine Learning

As with other contact center applications, AI is being combined with machine learning to continuously evolve and reflect the environment to ensure it remains accurate and up-to-date. In the realm of cyber-security, this means being able to continuously define and identify anomalies. This level of protection identifies an event, trigger or consequence based on specific data. Automatic detection and blocking can then happen almost simultaneously for protection in real-time, rather than after the breach has occurred. This technology also reduces false alerts that can waste valuable time and money. This will translate to a reduced workload, enabling an organization’s IT team to focus on other issues that require cognitive problem solving or manual intervention.

AI and machine learning are also being blended and integrated into other areas of contact center technology, such as routing, which can also support greater security. With customers being intelligently routed to agents, there is a significantly reduced risk of social engineering attacks on agents who lack customer insights and history.

Criminal contact centers would seem to be a terrific opportunity for voice biometrics technology, an anti-fraud solution which focuses on building unique voice fingerprints of known criminals and applying special anti-fraud screening to future calls from individuals who match those voice profiles.

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